Dynamic resource location coordination control system

ABSTRACT

A system and method for location centric activity leveraging convergence control of vectors having both a time and space domain. Additionally, the system executes the control of mobile and dynamic resources by controlling the dispatch of primary tasks with intermediate secondary tasks to enhance system efficiency and effectiveness. The location convergence of multiple mobile resources is vital to the realization of high-accuracy location determination and therefore high-accuracy inference and contextual relevance.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of provisional patent applicationU.S. No. 62/648,952 filed on Mar. 28, 2018 and U.S. Ser. No. 16/367,203filed on Mar. 27, 2019 both titled “Dynamic Resource LocationCoordination Control System”, and hereby incorporated by reference intheir entirety.

This patent document contains material subject to copyright protection.The copyright owner, also the inventor, has no objection to thereproduction of this patent document or any related materials, as theyappear in the files of the Patent and Trademark Office of the UnitedStates or any other country, but otherwise reserves all rightswhatsoever.

FIELD OF INVENTION

The present invention relates to mobile resource equipment operable toperform primary and secondary tasks that are specifically operated inaccordance to parameters that are location-specific including throughthe coordination and scheduling control system to leveragemulti-dimensional location vectors (i.e., space domain) with each vectorcomprised of a second dimension representing the time domain, includingcolor (or other digital, discrete though greater than binaryrepresentation) of time between a known starting time and known endingtime. The multiple time-domain and space domain location coordinationcontrol system has suitability in a wide range of location-dependentapplications ranging from transporting people or containers, operatingand/or characterizing modes (whether it be for equipment or people),cleaning, shopping, etc. in at least one geofence location havingembedded known locations further having embedded sub-areas of at leasttwo dimensions (e.g., X, Y) in which pathways (i.e., physical space“space”) exist as represented by multi-dimensional location vectors toincrease task performance effectiveness.

BACKGROUND OF INVENTION

Prior art includes the utilization of vectors to represent travel inmulti-dimensional (X,Y,Z) space domain, but not without significantcalculation intensity to determine convergence or intersection points ofmultiple vectors along a pathway for BOTH time and physical space, asthe prior art use of vectors doesn't provide a method for which thevectors can be rapidly (and visually) represented such that convergenceof multiple vectors (aggregated as a pathway for a particular mobiledevice) can leverage pattern-recognition to identify convergence pointsin approximately the same time domain. In other words, the prior artcan't utilize pattern recognition methods to rapidly achievecomputationally efficient means in identifying convergence orintersection points that occur at approximately the same time.

Prior art such as scheduling systems for task management also fail tofundamentally integrate resource optimization in which tasks are bothlocation and time constrained.

A need exists to maximize the utilization rate of mobile resources,including when the mobile resource has the opportunity to perform bothprimary and secondary tasks that are location-dependent exist includingthe objective of maximizing task effectiveness beyond simply optimizingmobile resource utilization.

SUMMARY OF INVENTION

The present invention is a dynamic resource location coordinationcontrol system “DRS” with integral and networked functionality amongst afleet of mobile resources operational to perform both primary andsecondary tasks.

A further object of the invention is to enable the control system to becomputationally efficient with a multi-domain vector system having bothtime and space domain optimized for pattern recognition in determiningvector convergence for approximately equal time domains.

Another object of the invention is to rapidly identify convergencepoints within the approximately same time domain of multiple mobileresources.

Yet another object of the invention is to control the operations ofmobile resources that are executing tasks in a dynamic manner specificto the location based on activity pathways represented by themulti-domain vectors.

Another object of the invention is to retroactively utilize themulti-domain vectors to improve location-specific predictions in thefuture in real-time, such that the accuracy of predictions is increasedby at least 5%.

Yet another object of the invention is to integrate components andsystem design features to create and coordinate active preventativemeasures in response to system detection measures.

An object of the invention is optimizing the performance of primary andsecondary tasks such that the secondary tasks have minimized noiseimpact, particularly when based on location-specific conditions in whichthe secondary task should be executed.

Yet another aspect of the invention is to accelerate identification ofclustering convergence to then enable superior pattern recognitionwithin the resulting clusters.

Another objective of the invention is to improve a wide range ofapplications that benefit from improved pattern recognition driven bylocation-based clustering to enhance contextual relevance and accuracyof big data through location derived mode (segmentation), decreasecross-contamination in public spaces, task performance includingcleaning and logistics, optimize routing, and to reduce task performancecost through the combination of increased utilization rates and reducedcapital costs.

Yet another objective of the invention is to leverage distributed ledgerfor comprehensive location tracking by utilizing dynamic encryption anddecryption keys based on location specific modes for each mobileresource thus ensuring privacy while improving contextual relevance foreach mobile resource to increase task effectiveness.

Yet another objective is to apply the core invention of locationconvergence in both time and space domain for multiple overlappingmobile resources to achieve numerous benefits including reduced fraudincidences, infection rates, task specific use of capital resource andits associated greenhouse gas emissions.

All of the aforementioned features of the invention fundamentallyrecognize the coupling of location convergence in the time and spacedomain to vary the operations of mobile resources.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a top view illustration, having multiple scenarios, of vectorswith probability gradients.

FIG. 2 is a data structure illustration for locations relative togeofence positions.

FIG. 3 is a data structure illustration for location specific recordsusing mode as encryption key within a distributed ledger database.

FIG. 4 is a cross-sectional view of a mobile resource.

FIG. 5 is a data structure for the mobile resource.

FIG. 6 is a data structure for the mobile resource use case of energytransporter.

FIG. 7 is a data structure for the mobile resource task schedulingcomponent.

FIG. 8 is a top-view illustration of nested geofences.

FIG. 9 is a physical and data structure of the mobile system dynamicrouting and scheduling system component.

FIG. 10 is a top view illustration depicting vectors converging in bothtime and space domains.

FIG. 11 is a data structure for the mobile resource use case ofinfectious disease.

FIG. 12 is a top view of the mobile resource pathway options between afirst location and a second location.

DEFINITIONS

The term “vector convergence” is the intersection of multiple vectorswithin an overall travel pathway in approximately the same time (i.e.,within a parameter of time threshold) and approximately the same space(i.e. within a parameter of distance threshold).

The term “color” is an at least one additional dimension added to thevector representing a travel pathway in at least a two-dimensionalspace, such that vectors show in a visual manner the intersection oftravel pathways for multiple mobile resources where the “color” is anyvisual or digitally represented parameter in which a pattern recognitionmethod finds at least one point having location convergence in time andspace domain with a computational efficiency of at least 10% lower thana system having vectors without color.

The term “location convergence in time and space domain” is theintersection of each mobile resource pathway as represented by vectorsor geofences with vectors in approximately both the same time and spacedomains.

The term “retroactive” is the determination of any parameter, notablylocation, such that looking back in time improves data accuracy, reducesprobability errors, and reduces frequency of data gaps with the specificintent of providing superior location accuracy in the future even in thepresence of data gaps (i.e., incomplete vector pathway for the travel ofa mobile resource).

The term “mode” is a characterization of a mobile resource intosegmented context by at least two locations, one of the currentlocations and the other of a dynamically altering location as a functionof time, co-located mobile resources, or adjoining geofences. Thepreferred embodiment determines mode through the utilization of at leastthree locations comprised of current location, past location(s), futurelocation(s) centered around a specific user. The particular preferredembodiment determines mode by at least three locations for multipleinteracting users.

DETAILED DESCRIPTION OF INVENTION

Here, as well as elsewhere in the specification and claims, individualnumerical values and/or individual range limits can be combined to formnon-disclosed ranges.

Exemplary embodiments of the present invention are provided, whichreference the contained figures. Such embodiments are merely exemplaryin nature. Regarding the figures, like reference numerals refer to likeparts.

The invention significantly reduces the incidence and contamination ofcontagious disease states across the combination of multiple time andspace domains.

Location Determination Functions

A critical feature of the system is to determine the location of amobile resource accurately, at least relative to a location in which atask must be performed. An even more critical feature of the system isto determine converging intersection points on a time and space domain(i.e., overlapping vectors predominantly matter when they occur inapproximately the same time). It is an object of the system to representlocation, as represented by a vector when it is precisely and accuratelyknown in a gradient (preferably a color, or as known in the art anynumerical representation of a color or pattern that varies as a functionof time (such that like colors represent like time) and from the startof the vector to the end of the vector based on time. When location isnot precisely known, it is represented by a geofence. The geofence isoptimally represented by a varying probability gradient, in which theprobability gradient provides a relatively higher projection of actuallocation likelihood. The probability gradient is established by thecombination of historic records establishing a pattern of travel timesbetween known precise locations (e.g., points in which known tasks areperformed at known locations, or GPS determined locations in which it isknown that location error is less than a specific error threshold limitin percentage or absolute length) such that future times in which travelbetween the same known locations enables an increased accuracy ofprecise locations in between those same known locations (that otherwisehave location errors that are beyond the specific error threshold limit.The location vector has an integral color (or pattern) that varies alongthe time domain, such that the key differentiation is a visuallyrecognizable similarity function (i.e., the human eye or graphicalprocessing unit recognizes the color or pattern at a point ofconvergence as being approximately identical, at least within asimilarity threshold limit). When the location has a degree ofuncertainty the location vector is represented by a geofence vectorhaving an integral color (or pattern) along with an optional probabilityprofile (or a probability map of individual locations based on knownprior high-certainty locations, or additionally in combination withfuture known (or anticipated) high-certainty location(s) when lookingforward in time). This feature of the invention also enables superioraccuracy of retroactive location gap filling.

Turning to FIG. 10, FIG. 10 depicts vectors representing the movementbetween locations, such as the first location A302 to the secondlocation E302, with each individual vector represented both bydirectionality and color gradient (in this case as shown in black andwhite with shades of grey). The exemplary instance shown clearly showsconvergence of space and time (as indicated by dashed circles) atconvergence point 112.3 for vectors 113.11 and 111.1, as well asconvergence point 112.1 between vectors 111.13 and 111.1 (and clearlynot 111.12 due to different color, meaning no convergence of time butonly space). Convergence point 112.2 at location E302 is for vectors111.1 and 114.11.

Turning to FIG. 1, FIG. 1 depicts four scenarios of a vector starting atfirst location A302 to an ending location E302. Each vector can rangefrom Scenario 1 that depicts an instance where the precise location isknown from start to finish, Scenario 2 that depicts a region in whichprecise location is not known (such that the probability varies from anintermediary location in which two precise tasks occur at a preciselocation and precise time but in between those two tasks preciselocation becomes unknown and the time in between each task establishes arelatively linear probability gradient, Scenario 3 is similar toScenario 2 except that probability gradient is not linear (such as wherea wireless signal has more variation such as created by interference ofdoors, walls, etc.), and Scenario 4 in which no known information isavailable between the two precise tasks and therefore the probabilitygradient is large and relatively non-deterministic in between the twoprecise tasks.

Known locations are established by multiple means including theperformance of a task through a device having a known location in whichthe device communicates via wired (e.g., Ethernet) or wireless (e.g.,low-energy Bluetooth, NFC) such that the highest accuracy of location isestablished by either the fixed location Ethernet or the wireless methodwith the lowest range.

It is a feature and an object of the invention to fill in the gaps oflocation knowledge, whether the prior knowledge of a precise location isnot known at all or the location is represented in the past by ageofence (preferably with a probability gradient), such that the gap(s)is replaced by a precise vector (or simply a geofence having a moreprecise probability gradient). One instance in which precise locationknowledge is desired, even though the mobile resource is no longer atthat location, is in establishing likelihood of cross contaminationretroactively after an infection has been obtained, transaction fraudprior to the shipment of an ecommerce order, and projections of futurelocation when resources travel relatively repetitive routes (such asemployees within a hospital). It is a feature and an object of theinvention to maintain historic location data as obtained in real-timefor each location (preferably on a continuous vector mapping) with a setof non-real-time adjusted location data such that the combination of thereal-time location and the non-real-time location adjustment provides amore precise predicted real-time location. It is understood that thenon-real-time location adjustment is a function of at least one of theprecise location or the real-time location point. The non-real-timelocation adjustment is preferably also a function of the actual mobileresource and optionally also as a function of time. The particularlypreferred non-real-time location adjustment is further as a function ofa known presence of an at least a second mobile resource object or an atleast first inanimate object (which can have more than one positione.g., door that is open or closed) that in fact impacts the locationprecision when the first mobile resource is in proximity with the atleast a second mobile resource. Retroactive improvements of locationdata occur by analytical inclusion of subsequent data records throughknowledge of then future performance of primary tasks or secondary tasksat known locations. More specifically, there are gaps of precise (oreven general) location knowledge that occur (most notably from loss oflocation-determination signals e.g., global positioning systems “GPS”whether the GPS is an indoor or outdoor GPS or simply uses wirelesstriangulation as known in the art) such that during the occurrence of aparticular task (or event, such as opening a door as the mobile resourcemoves between locations) the precise location is not yet known. Yet,when the mobile resource performs a task or event at a both known timeand known location (or within a known geofence) this action establishesa probability profile for past tasks or events (in other words, if atask took place on the 2^(nd) floor of a hospital at 11:00.00 AM thereis a very low probability that the same mobile resource would be on afloor above or below the 2^(nd) floor at 10:59.50, especially if thelocation is not near an elevator or staircase, or if it is known withcertainty that no mobile resource was located in an elevator orstaircase in between those times). The inventive system uses both knownoccurrences of an at least one second task in terms of precise time andlocation, and/or known absences of tasks in terms of precise time andlocation, or combinations thereof to establish a location probabilityfor a first task in terms of precise time and location that is afunction of precise time and location for the at least one second task,and/or for the known absence of tasks within a precise location (orgeofence) within a known time range.

It is another object of the invention to create a comprehensive recordof location as a function of time for each mobile resource, recognizingthat a mobile resource can be a person guided device, a semi-autonomousor autonomous guided device, or even a person qualified to executeprimary and/or secondary tasks such that the person is mobile and thedevice that the person uses has no independent means of movement. Whenlocation data is for a specific person the maintenance of personalprivacy is essential. Yet, serving the specific person (i.e., operator)with superior precision requires extraction of location knowledge. It isan object of the invention to segment the access to personal locationdata notably by leveraging a distributed database (with multipleencryption keys, preferably with access control by a function of both amode and a geofence) also referred to as a distributed ledger such thatthe distribution of location data is by at least one of a) mode, b)geofence, or specific data server representing location data by acombination of mode and geofence. It is recognized that specificgeofences maintain data that is not only proprietary to the individualperson but also business or contextually sensitive to the hostbusiness/purpose of that specific geofence. One exemplary host businessis a hospital (or medical campus) in which strict privacy laws provideguidance and control of access to sensitive location data linked to aspecific individual person. Yet, it is an object of this invention torepackage this highly sensitive comprehensive location into reducedsensitivity packets. The distributed ledger of location data issegmented by mode (e.g., exercise, home, work, medical, personal, etc.)even within a private geofence. Specifically, the distributed ledgerdatabase preferably utilizes rules-based logic and mode functions tocreate location offsets in which the offset is a function of time, mode,and/or rules-based logic or combinations thereof. The purpose of alocation offset is to ensure that the absolute location can't beaccessed, yet relative location data can be accessed. One exemplaryincidence in which relative location information is desired but absolutelocation information needs to remain private is an exercise applicationsuch that the exercise application desires to calculate or track dataincluding number of steps, calories burned, velocity and/or accelerationrates etc. yet the actual instance that the absolute location is withina hospital or an armed forces forward operating base must remain secret.In the context of a hospital environment a staff member such as aphysical therapist is authorized to track physical activity but shouldnot have knowledge of specific activities such as bathroom activity,medical testing procedures, etc. In other words, access to specificlocation information is a function of at least mobile resources (i.e.,the mobile resource requesting information and the mobile resourceowning the information). Location offsets are preferably encryptedutilizing a non-linear or linear function that can be further switchedbetween varying non-linear or linear functions in accordance to furtherrules-based logic as a function of time. Personal location data issegmented into access type between public and private. Private data isprovided within the guidelines of secure access in packets that includeat least: a) summary of mode data, b) redacted data of specific modessuch as healthcare specific modes, c) segmentation data by mode outsideof healthcare specific modes, and d) data containing offsets.

Turning to FIG. 2, FIG. 2 depicts the data structure for both contextualrelevance as well as privacy constraints. Each location A302, B302,C302, D302, and E302 has a record with a parent-child relationship to atleast one geofence. It is understood, though not depicted, that anylocation can have multiple parent-child relationships to multiplegeofences with further record connectivity to Public Geofence Database205.4 (as connected to location B302 which enables less severe accesscontrol relative to the Private Geofence Database 205.1 as connected tolocation C302). The utilization of a Home Geofence 302.1 (as connectedwith location A302) establishes a fundamental set of privacy rules inaccordance to the Rules Database 20 in addition to the Home Geofence302.1 also having a parent-child relationship to establish the geofenceas within an at least one Home Mode with the Home Mode Database 205.3.Home Mode is particularly important in terms of privacy and therefore itis understood that specific Encryption function 209, which is a functionof time, rules and mode, is used to severely limit access of such datawithin a Distributed Ledger Database 10. The Mode Database 20.2, anotherdatabase similar to the Rules Database 20.1, maintains records ofgeofences in which relevant modes are possible and respectively relevantrules are possible. Information gathered within a home has particularrelevance amongst a family (largely without restrictions) but alsoprovides important marketing consumption info as one exemplary withanother exemplary being cleaning or cross-contamination requirements).The performance of cleaning or decontamination tasks in betweenlogistics transport from a first location to a second location within ahome. A Work Geofence 302.2 (as connected to locations D302 and E302)also has specific meaning therefore requiring differentiation from othergeofence categorizations.

Queuing and Routing of Secondary Task Functions

For the purpose of this invention, a primary task is a task that has afirm commitment to completion within at least a specified time range(beginning at an earliest to complete time through a latest mustcomplete time). Additional parameters for a primary task include havingat least one parameter selected from the preferred start time, preferredfinish time, and must start time. A secondary task is a task that doesnot yet have a firm commitment to completion within at least a specifiedtime range, but rather has parameters that provide a relative rankingamongst other secondary tasks (i.e., set of secondary tasks) so as toprioritize the sequential processing of secondary tasks. Given that amobile resource is often a capital resource (or a human resource) it isadvantageous for the mobile resource to maximize the amount of timespent performing both primary tasks and secondary tasks (whileminimizing excessive travel time by minimizing routing distance betweena first and second location). One exemplary secondary task is reducingthe potential for cross-contamination (amongst sequential touchinteractions whether it be by a mobile resource, any person (whether itbe employee, customer, or guest) at a specific location (or physicalobject at the specific location e.g., doorknob, sink, elevator buttons,etc.). It is anticipated that a secondary task becomes a primary task inthe event that the secondary task has not been performed prior to aspecific time, within a certain time interval, or at a rate ofcompleteness within a certain time interval.

In most cases the operating and capital cost of mobile resources (i.e.,roaming robotic devices including automated guided vehicles; people inwhich tasks are conducted at varying locations and a pathway is taken tosequentially perform the tasks) moving from a first location to a secondlocation is a dominant cost. Given that travel costs (i.e., both directand indirect such as amortization of equipment) are significant, it isan object of the invention to enable the mobile resource to perform bothprimary and secondary tasks with minimal excessive non-productive travelor time losses. It is understood that particularly for human resources athird category of tasks, hereinafter referred to as “optional mobileresource objectives” are optionally factored in while determining thesequencing of primary tasks and secondary tasks including the travelrouting in between the primary and secondary tasks. An instance of theoptional mobile resource objective is either the minimization of totaltravel distance, achieving a total travel distance threshold but thensubsequently minimizing an excess beyond the total travel distancethreshold, or maximizing a total travel distance such that the act oftraveling is in fact an objective (i.e., security control).

Turning to FIG. 12, FIG. 12 depicts the mapping area between a firstlocation A302 having a required first primary task and the endinglocation E302 having a second primary task with the intermediatelocations of B302, C302, and D302 all having consideration of secondarytasks to be completed. In between the first location A302 and the endinglocation E302 is a mapping of areas in which secondary task has alreadybeen completed being the Secondary Task Completed Mapping 711.1 and711.2, as well as areas in which secondary task has not yet beencompleted being Secondary Task Non-Completed Mapping 712. The mobileresource 690 is equipped with at least one vision sensor 692 to at thevery least determine where obstructions 691.1 and 691.2 exists thattrigger dynamic scheduling of secondary tasks by the mobile resource 690including in instances where delaying travel movement between the firstlocation A302 and ending location E302 after the obstruction 691.1and/or 691.2 would at worst force the completion of the primary task atending location E302 to be after its required completion time.Therefore, the routing and scheduling of the secondary tasks aremodified in real-time so as to avoid obstructions at least when suchdelay in travel movement would create missing completion of the primarytasks at ending location E302 (or any subsequent locations in whichprimary tasks are scheduled) prior to its respective required completiontime.

It is further an object of the invention for the dynamic routing andscheduling system to dispatch both travel commands and task performingcommands of secondary tasks as intermediate activities. The dispatchingof secondary tasks, also referred to as an at least one intermediateactivity, to a specific mobile resource (amongst the fleet of availablemobile resources) is based on multiple parameters including thesuitability, qualification and/or performance rating, cost of operationrelative to other mobile resources available, anticipated, or that canbe scheduled for a later time (but still earlier than the performed taskmust complete prior time). It is understood that the suitability andperformance rating is preferably a function of both the mobile resourcecapability and also its operator (when such operator is required) as theresulting performance of the secondary task (as well as primary task) isbased on the unique combination of operator on a specific mobileresource (i.e., increasing amounts of training and actual deploymentexperience on a specific mobile resource by a specific operatorincreases the certification/qualification). The mobile resource has arange of skills or tasks that it can accomplish with relative andabsolute qualification/certification ratings relative to other mobileresources.

One exemplary application is the utilization of the DRS within ahospital or public space such that cross-contamination of a contagiousillness/disease can take place. There is a practical limit in terms offrequency of cleaning/disinfecting that demands a probabilistic orinterval-based scheduling to take place. The urgency and therefore theprioritization of schedule is a function of parameters inclusive ofprobability of disease cross-contamination, seriousness (and/or costrisk) of health consequences to a specific disease in which a mobileresource has a probability of disease cross-contamination, time intervalsince last antimicrobial disinfectant (i.e., relatively instant kill)treatment, time interval since last antimicrobial persistent treatment,strength of antimicrobial persistent treatment, projected intervalbetween sequential touch interactions, etc.). The mobile resource mustbe capable of performing all primary and secondary tasks that becomescheduled or queued onto that respective mobile resource schedule. It isunderstood that the execution time of a primary task and/or a secondarytask by a specific mobile resource can vary in accordance to theindividual capabilities of the task executing equipment onboard to themobile resource as well as operator performance. Scheduling of secondarytasks also takes into account additional scheduling parameters for thesecondary tasks objectives by featuring bonuses for early completion,and penalties for late or partial completion within a mobile resourcespecific aggregate revenue parameter (which is the summation ofindividual bonuses or penalties, whether those be based on actual orprojected dynamic routing or schedule).

Turning to FIG. 7, FIG. 7 depicts the data structure in a parent-childrelationship from a mobile resource 690 perspective notably as it wouldtravel from a first location A302 to second location B302 in itsachievement of respectively assigned primary tasks 301.1 and 301.2. Thedata structure clearly shows at least one secondary task 302.ab.1 havinga data structure/name indicating that the secondary task is relevantbetween the first location “A” and the second location “B” which isqueued for completion within a queued event 306 series of records. Eachof the primary tasks have scheduled execution times within a series ofscheduled event 305 records that are further assigned to a specificmobile resource 690 (including the ability to dynamically assign to themobile resource in real-time based on availability and/or proximity ofmobile resource to actual location. The scheduling system 300coordinates the execution of both primary tasks and secondary tasks allof which have records within the database 205 where each task is firstan unscheduled task 309 becoming linked to a specific unscheduled event307 (as a function of the task itself) and then finally scheduled andassigned to a specific mobile resource 690.

The aforementioned process is virtually identical to a cleaningtreatment, whether it be a dry or wet process including vacuum cleaningoperations. A preferred embodiment of the invention is to determine avector pathway in both the time and space domain for the mobile resourcesuch that the specific sub-area within the geofence or more specificallywithin the location internal to the geofence has an at leasttwo-dimensional contaminant map (that shows either actual physicaldetection of contaminants, or projected contaminants based on actual orhistoric traffic patterns including taking into account the passage ofmobile resource(s) including guests, patients, staff, etc.). It is agoal of the invention such that dynamic routing system, particularlywhen time constraints exist between at least two primary tasks limit theability to perform a decontamination process (e.g., cleaning, vacuuming)within the entire sub-area minimizes the potential for crosscontamination or at least minimizes the cost risks due to potentialcross contamination. Therefore, the dynamic routing system coordinates avector pathway that optimizes the decontamination process byprioritizing the decontamination of specific vectors or sub-geofenceswithin the specific sub-area. It is further a feature of the inventionto leverage camera or sensor images, whether it be from fixed positioncamera or sensor devices or roaming camera or sensor devices such asmounted on mobile resource equipment. The system utilizes real-time,historic, and retroactively corrected image mapping to create an updatedprobability map within the sub-area (that is within the location and/orgeofence) assembled by at least one camera or sensor device butpreferably from a stitched image assembled from multiple camera orsensors from multiple mobile resource equipment plus additional fixedposition camera or sensors. The system has at least two maps of thesub-area, one of which is the contaminant probability map and the otheris the mobile resource pathway map of assembled vectors (plus taskcoverage offset preferably represented by a vector having a width tocover the task coverage offset). A particularly preferred embodiment hasan at least 3′ sub-area map for each known disease state being trackedsuch that a contaminant source pathway provides a time stamped (i.e.,utilize the time color code for incidence, which can optionally beoverlaid by intensity representing the frequency of exposure) vector.Each of the maps are preferably represented by the inventive feature ofthe vector having a time (color code) within the space domain.

Turning to FIG. 8, FIG. 8 depicts the potential (and typical)overlapping of geofences such that exemplary locations B302 and C302 areexternal of a building represented as a whole by geofence 302.3. Thebuilding can have multiple floors or distinct areas, such that thedecontamination requirements are distinct, such as a first distinct areabeing a hospital ICU as location geofence 302.2 having rooms A302 andF302, and a second distinct are being patient rooms in geofence 302.1having rooms D302, E302, and G302. It is understood that different typesof geofences (i.e., physical structures) have the commonality of nestedgeofences having further nested locations within those geofences.

Yet another feature is that the “roaming” mobile resource can be aperson, a robot that has certifications or qualifications as well asranking of performance in comparison to other mobile resources. Thedispatcher of secondary tasks has an acceptance or rules-based systeminterface so as to selectively establish a preference or a rejection ofperforming secondary tasks as made available by the dynamic routing andscheduling system. It is understood that the performance of secondarytasks may lead to the missing of a primary task. It is also understoodthat rejecting a secondary task may lead to the secondary task becominga firm scheduled primary task by a set of parameters including mandatorycompletion. The DRS seeks to minimize the cost of potential risksassociated with missing the performance of primary tasks or secondarytasks at a system aggregate level. Such cost of the potential risk is afactor of many parameters notably the seriousness of consequencesassociated with either missing or delaying the execution of a primary orsecondary task. In general it is understood that primary tasks shouldnever be missed due to the scheduling of a secondary task.

The notion of primary and secondary tasks can be in virtually anylocation; however, it is most relevant in locations that are publicspaces such as airports, train stations, hospitals, office buildings,shopping malls, schools, restaurants, and neighborhoods where activitiessuch as cleaning, sterilizing, cutting lawn|veggies, logistics includingenergy storage take place).

Turning to FIG. 4, FIG. 4 depicts a cross-section view of the mobileresource equipment 690 (though it is understood that the equipment canbe replaced by a person carrying components to execute both a primarytask and a secondary task which are shown as components within theprimary task storage 505.1 or secondary task storage 505.2 (with furtherdifferentiation being between the interior space (i.e., not requiringdirect physical access) and exterior space (i.e., requiring directphysical access or having the ability for movement from an interiorspace to an exterior space through means known in the art). Aparticularly suitable method of providing access from interior storageto exterior storage (or access) is for fluid storage in an interiorspace 510.2 through a valve for fluid loading/unloading 525.2. Analready exterior fluid storage 510.1 can provide fluid access for fluidloading/unloading via an immediate placed piping and control valve asknown in the art 525.1. One exemplary secondary task is decontaminationvia contaminant treatment component 520 such that movement of the mobileresource equipment 690 can easily achieve its primary tasks withintermittent execution of secondary tasks (e.g., decontamination orcleaning). The mobile resource equipment 690 moves from a first locationto a second location via means as known in the art, which includes themost likely wheels 689. It is understood that other means of movement asknown in the art include flying, sliding, or conveyors.

Another feature of the invention is the notion of a dynamic speed suchthat the speed required to maintain traffic flow, if too fast (or tooslow) for performing a secondary task reduces the secondary taskeffectiveness (therefore, want to keep track of not only requirement forperforming secondary task within a location/geofence, but also theeffectiveness of performing the task/event/operation such that thesubsequent requirement to repeat the secondary task in the future isaccurately represented (i.e., how well the task for the specificlocation was performed in the past has an impact on the next occurrenceof that same task for that same location). A particularly importantinstance of this task is centered around reducing thecross-contamination of hospital acquired infections “HAI”. A range ofpotential intermediate events (i.e., tasks such as surface cleaning,deep washing, UV disinfecting etc.) have corresponding discreteincremental values associated with the relative degree of reducingprobability of cross-contamination, as well as a range of time impact(i.e., the potential persistence of that task post performing the task).The combination of degree of reducing probability of cross-contaminationwith time impact persistence provides the DRS with a rules-based logicin which the intermediate event (i.e., secondary task) switches to aknown scheduled event (it must be cleaned again by a certain time i.e.,becomes a primary task). The DRS also tracks utilization for a specificlocation/geofence to base that utilization rate in determining thefrequency or time interval of a task (e.g., cleaning rate), which isfurthermore also a function of the serious nature of disease andprobability of being contaminated.

All of the aforementioned is involved in the routing function for a soleprimary task. Other parameters and functions involved in the dynamicrouting include parameters as a function of (utilization, detection ofabnormalities, time since last occurrence of primary task within themultiple pathways that can occur, occupancy along the pathway, occupancyadjacent to the pathway in which noise could impact); cluster of devicestraveling the pathways communicate detection of abnormalities such thatsubsequent mobile resources travel pathways to correct for abnormalities(i.e., a specific area is dirty or different than it was in the past).

One exemplary instance is for a cluster of mobile resources performingcleaning, such that the cluster communicates to a system that tracks theaggregate/collective pathways so as to direct mobile resources tocomplete the determined gap(s) of non-cleaned pathways (e.g.,scheduling/routing of secondary tasks). The mobile resources can also bemoving product from point A to B (such as restocking inventory ofstores) and/or moving people from point A to B. The specific objectivesof a secondary task, such as when it involves the potential forcross-contamination, is the probability of a mobile resource passing oncontaminants within the specific geofence or to subsequent mobileresources (as well as non-known patients, guests, staff, etc.) is a f(t,time since last exposure to a known contaminant, time since lastcleaning of objects interacting with known contaminant). Other secondarytasks include cleaning, vacuuming, moving packages, being social, doinga task such as folding laundry, cutting veggies, etc.

Turning to FIG. 5, FIG. 5 depicts the data structure from the mobileresource 690 perspective in its execution of primary tasks 301.1 asshown. It is understood that the data structure is virtually identicalfor secondary tasks (though not shown). FIG. 5 is exemplary of the usecase whereby secondary tasks are cleaning and/or decontaminating ofnature (which can be used interchangeably). The mobile resource 690during its movement between locations as depicted to or from a specificlocation identified by its location record 302 is through at least onemobility pathway 225 with each occurrence represented by a vector pathrecord 226 within a geofence therefore having a parent-childrelationship with that geofence having a geofence record 300. The totalarea (or volume when decontamination is required on walls as well andnot just the floor) requiring decontamination is virtually alwaysgreater than the single-pass decontamination achieved by the mobileresource 690. Therefore it is vital and a feature of the invention tomaintain a record of each instance of decontaminated area such thatsubsequent passes within the geofence have a different mobility pathway225 with the objective of achieving the secondary task (ofdecontamination) for the entire total area (with minimal additionaltravel time and distance) so as to not hinder the successful completionof the primary task 301.1 prior to its required completion time of whichat least one primary task 301.1 is scheduled/assigned to the specificmobile resource 690. Each mobile resource 690 has an optional operatorresource 697 and in virtually all instances has transported resourceseither that are fundamental to the mobile resource 690 itself or beingplaced within the various storages as per FIG. 4 in which the primarytask is a logistics function including the movement of a fixed assethaving a fixed asset record 215 from a first location to a secondlocation or in which the primary task is to perform an operation eventhaving an operating event record 101 on a fixed asset 215 in thelocation 302. The presence of a guest having a guest record 228 having amobility pathway 225 takes a specific vector path having a vector pathrecord 226 within a specific geofence having a geofence path record 227where the guest has a mode identified via a mode record 222 (such thatthe mode is indicative of the guests specific impact on environmentalcontamination while protecting the privacy of the guest itself). Theguest mobility pathway 225 and its mode 222 records may optionally leadto an unscheduled task 309 that can range from needing immediateattention through an operating event record 101 or remain in-queue on anas required basis. The scheduling of primary tasks is driven, in thisuse case, by the combination of contaminant events 101 which are furthercharacterized by its contaminant source 105 having distinct thoughpotentially linked via parent-child relationships to at least theprimary task 301.1 (so as to define the parameters in which the mobileresource operates it decontamination procedures once assigned to thespecific mobile resource) and also linked (though not shown) to thelocation record 302 in which the contamination took place. The location302 is within a geofence 300 and both have their records stored within adatabase 205 that is further located within a database server within adatacenter 200 (that can be on-site or off-site).

Turning to FIG. 6, FIG. 6 has the operational parameters when the mobileresource (though not shown) has its secondary tasks centered aroundmovement of energy storage assets from a first location to a secondlocation whether it occurs concurrently of primary tasks or if in factthe otherwise secondary tasks become primary tasks through mobileresource reconfiguration from a primary to a secondary task fulfillmentdevice. The movement of energy storage assets, which can be electrical,fuel, or thermal means as known in the art, are characterized by a setof parameters including electricity production historic records as f(t),electricity consumption historic records as f(t), demand consumptionhistoric records as f(t), demand production historic records as f(t),vehicle transport route historic records as f(t), electricity ratestructure historic records as f(t), electricity demand rate historicrecords as f(t), vehicle transport rate structure historic records asf(t), transactions inflow:outflow ratio historic records as f(t),distance & route from rate structure historic records as f(t), vehicletransport cargo utilization historic records as f(t), vehicle transportenergy utilization historic records as f(t), electricity productionprojected records as f(t), electricity consumption projected records asf(t), demand consumption projected records as f(t), demand productionprojected records as f(t), vehicle transport route projected records asf(t), electricity rate structure projected records as f(t), electricitydemand rate projected records as f(t), vehicle transport rate structureprojected records as f(t), transactions inflow:outflow ratio projectedrecords as f(t), distance and route from rate structure projectedrecords as f(t), vehicle transport cargo utilization projected recordsas f(t), and/or vehicle transport energy utilization projected recordsas f(t).

Retroactive Location Gap Filling and Accuracy Improvement Functions

There are significant issues associated with location accuracy, notablysuch as within indoor locations or within outdoor locations ofparticular note being an urban (e.g., high-rise) area. This demands ahigh-precision method to correct for these location gaps, though thecorrection process doesn't necessarily need to take place on a real-timebasis. One exemplary instance is the ordering of an object on-line suchthat the business fulfilling the order wants to verify the order issuerin which the then location knowledge is substantially improved as amethod to prevent or limit fraud by way of retroactively filling in theknown location gaps. Since the taking of the order is not alwaysconcurrent to the fulfillment of the order, and more particularly theshipping with receiving of the items ordered, a retroactive update ofthe location gap prior to the actual receiving of the items orderedprovides the ability to prevent fraud from taking place (and mostimportantly avoiding the bulk of the costs associated with a fraudulenttransaction).

The inventive system is capable of “gluing/stitching together” a mobileresource pathway utilizing a location specific device such that acomprehensive location specific data profile can be created even thoughreal-time location signal may not be known in real-time (i.e., could bebecause of indoors, etc.). The system features a “retroactive” locationengine application that establishes location confirmation of locationpathways having uncertainty where the uncertainty is at least one of thetime or space domains. This is a critical feature of the system, as thethen future (but now past in terms of retroactive location correction)enables superior accuracy in establishing the mode (of the mobileresource, whether a mobile piece of equipment or a “roaming” person) andthus subsequently all mode dependent or deterministic data. Filling gapsin location data, which are a reality of location determining methodswhether it be an inherent variability of the location determinationmethod (i.e., sensors, Global Positioning System “GPS”, wirelesstriangulation), errors due to internal reflection or lack ofline-of-sight GPS, or just dynamic interference attributed to mobileresource position (or co-located people or objects) creating dynamic orstatic error, is vital to the confirmation of task performance, orestablishing mode for the mobile resource. Retroactive location gapfilling enables improved accuracy for essentially all future locationdependent data and operations particularly in an environment in which ahigh degree of repetition is performed by the mobile resource or otherentities in which the mobile resource interacts with.

Another exemplary application where precise location can beretroactively provided is for infection control. An instance ofinfection takes place at a second time that is both different and aftera first time at which the potential for cross-contamination has takenplace. Increased location accuracy is vital for determining time andspace convergence between multiple mobile resources, and theirlocation-specific operations. This is particularly important in anexemplary application of determining cross-contamination probability fordetermining infection exposure. The nature of most systems andapplications that will leverage the inventive location vectors have theexecution of primary and secondary tasks on a repetitive basis, thoughdifferent mobile resources (which each may have unique error factors)may execute the tasks from a first instance to a next instance, enablinga probabilistic model to be utilized to calculate or adjust forrepetitive and known (from historical data) gaps in data or errors inoffset of location reference points. A particularly important area forinfection control is within healthcare facilities. It is an advantagefor the DRS such that patterns are obtained for the majority of themobile resources (e.g., doctors, nurses, pharmacists, cleaning staff,food transporters, etc.) such that this historic database of locationsalso enables a historic database of location error corrections isaccumulated. Each of the aforementioned mobile resources also has asignificant quantity of primary tasks, which are often precisely trackedfor incidence time and location (such that the location is preciselyknown, as well as time). The convergence of multiple mobile resourceswithin a location threshold limit is vital to determining theprobability, limiting the cross-contamination potential, and managingthe performance of both primary and secondary tasks to statisticallyreduce the incidence of infections. The preferred embodiment of the DRSfor infection control includes the tracking of infection control assetsranging from disinfectants having immediate infection reset state(s),combination disinfectant with persistent reset state(s) as a function oftime since the identified potential cross-contamination or time sincethe resetting of state (e.g., cleaning, disinfecting, antimicrobialremedy, etc.).

Turning to FIG. 11, FIG. 11 depicts the data structure specific to theuse case of cleaning/decontamination tasks achieved as predominantlysecondary tasks 2250. The establishment of secondary (or primary) tasksare a function of persistent disinfection systems 120, non-persistentsystems 110, or general disinfection systems 115 (all having parametersof effectiveness that are disease specific across time since exposure ofcontaminant and since application of disinfection (whether general,persistent, or non-persistent)) at each location 300.1 coordinated bythe location system 105. The aforementioned systems are an integralcomponent of the server system at the datacenter 200 with each recordbeing part of a database 205. Each patient (used interchangeably withany person or source of contamination) record 101 has a record also partof a database 205. Each source of contamination has vectorrepresentation across time and space domains with each having a recordidentifying the cross-contamination pathway 215 further having aparent-child relationship with a specific location 300.1 (that can befurther defined as a geofence). Each contamination source (i.e.,patient) 101 is characterized by the type of contamination as containedwithin a diagnostics HEPA record 220 further containing individualrecords for each disease state in the infection control record 225. Thepatient disease state can be treated via infection control device 400such that the present infection control treatment and original sourceidentification of disease state 225 determines the specific nature oftasks 2250 required for reducing cross-contamination. A further featureof the system includes the disposal 220 of materials utilized in theinfection control device 400 at a second location 300.2 (or any otherlocation including the same first location) then becoming a potentialcontamination source at that second location. The treatment of infectioncontrol 400 can occur over multiple individual treatments each of whichcreates a consumption record 222.2. Non-patient specific items can beconsumed at a given location 300.1 as indicated by a consumption record222.1 with an original source record 225. Another exemplary of saidlocation specific consumption can be food (which is subsequentlyidentified as contaminated) with as yet unknown consumer.

Notably, this significant reduction in location gap or increasing inprobability likelihood, enable an at least one prior uncertain locationvectors or geofence (having an even larger probability uncertainty) tooptimally enable the replacement of geofences (i.e., representinglocation uncertainty) with known location vectors. It is understood thatthe resulting location vectors will likely have uncertainty in the timedomain, though a reasonable means to project vectors with more accuratetime domains (i.e., color) is to utilize average speed (whether it befrom historic data specific to the average of multiple mobile resourcesin the same geofence, location, or sub-area; or average speeds betweenlike distances on a historic basis for the mobile resource itself).Another inventive method to reduce location knowledge gaps is to includeprecise knowledge of locations within a geofence of additional locations(e.g., bathrooms, patient rooms, restaurants, water fountains) even whenthese additional locations are not directly relevant to the execution ofa primary or secondary task by the mobile resource within the specificgeofence, location, or sub-area. The ability to infer, whether it befrom known blind-spots within the aforementioned additional locations orcalculated by significant deviations from the norm in time spent withinthe geofence, location or sub-area increases the probability of themobile resource spending time within the aforementioned additionallocations. Since the additional locations are both known in terms ofrelative location and functionality relative to mobile resource(s), thisinclusion of data is important to leveraging particularly the knownfunctions in the respective additional locations in combination withmode data to determine and provide superior contextual information inthe execution of future decision making and/or inference engine. Inother words, the inventive features in the retroactive location engineproviding even simple knowledge of the mobile resource presence providesimportant information in establishing mode (or further contextualinformation utilized in the future) or determination of future outcomeprobabilities, as well as probability assessments of past activities(such as secondary tasks or events, or notably non-tracked activitiesthat are location deterministic e.g., probability of a second patienttouching a door knob that could have been touched by a first patientprior to the second patient and also prior a secondary cleaning taskbeing performed on that same door knob). The further inclusion ofhistoric records in which a mobile resource has visited, includingfrequency per unit of time, time interval (i.e., average, mean,shortest, longest) between similar events is vital to increasing theprobability accuracy for future retroactive location determination. Thisis further of importance when the mobile resource is within a geofence(i.e., building) in which a wide range of tasks can be performed (e.g.,eating, going to bathroom, shopping, exercising, etc.). In fact, theutilization of historic records for different locations, particularlywhen those locations have historic pathway records where the locationprobabilities have higher certainties (e.g., standalone restaurants,stores) create more accurate time of day probabilities and time intervalprobabilities of the function from known function of the aforementionedadditional location(s). The system is able to make assumptions infilling in location gaps by GPS vectors due to losses (or erroneouslocation determination) in GPS signal data (e.g., indoors, outdoors buturban downtown setting that creates errors due to GPS signal reflection,etc., traveling underground|airplane) and probability data estimatesparticularly in the probability of performing an intermediate (i.e.,secondary) task and for the purpose of predicting next activity (i.e., aprimary task or secondary task) or even for enhancing the accuracy ofhistoric activities (i.e., primary or secondary tasks) as improvedhistoric information enables more accurate real-time information.

The inventive system clearly has the capability of having “big brother”concerns when the mobile resources are people (i.e., human resources,guests, patients, clients, etc.). And yet, the more comprehensive andcontinuous data information on a precise location, or precise locationpathway, or even a precise location geofence with both time and spacedomain knowledge increases the contextual data segmentation andtherefore the accuracy by an inference engine. This poises real privacyconcerns and thus demands a further aspect of the invention which is aselective by location mode (with optional integral coupling withgeofence, location, or sub-area) to provide restrictive access (or atleast restrictive access to decrypted location data) to onlycontextually relevant data through ensuring only fragmented data accessas further characterized below.

Distributed Ledger for Privacy Functions with Selective FragmentedAccess

Intimate knowledge of location data, especially with detailed timedomain data, in the wrong hands can be utilized in harmful manners. Oneexemplary is soldiers using a fitness device having GPS tracking thatconveyed information on a secret forward operating base location. Toovercome the harmful use of precise location vectors with both time andspace domain information, it is a feature of the system to makeavailable only summary information when the information request isrelevant to a specific mode. An exemplary of this is database recordscontaining summarized data in which the requester obtains data with aGPS coordinate offset in some instances, a time offset in otherinstances, and in other instances both a GPS coordinate and time offset.A more specific example is for a mode equal to eating, which therelevant contextual information includes a) most recent eating eventprior to current time, b) average distance traveled to eating venue, c)average distance from home (or work) to eating venue, d) average timetraveled to eating venue, etc. Therefore, database access for eatingmode is limited to prepared summaries containing subsets of locationdata with precise time but GPS offsets for the vectors and geofencesthemselves. Other offsets utilized by other exemplary modes that alsodon't require absolute GPS coordinates, but only relative coordinates,vary the real GPS coordinates with offsets established through asecurity function as a function of time “f(t)” or some other parameterso as to protect the real GPS position of the resource. These activitiesinclude exercise, predicting walking|driving travel based on pasthistory (important aspects are type of pathway e.g., paved road,sidewalk, etc.; change in altitude being traveled, traffic lights,etc.), marketing (e.g., walking up and down aisles within store, orshopping mall) though the actual shopping mall doesn't specificallymatter (or have to be disclosed) in order to enable superiorinference/contextual relevance.

The distributed ledger (i.e., blockchain, or any other means ofsegmenting a database, or at least access to decrypted data, through atleast two encryption/decryption keys, though preferably anencryption/decryption key for each mode, has a comprehensive history oftime and geofence location (time and space domain) for each mobileresource (i.e., person/customer/patient), such that at least one of thegeofence location and/or mode is the key for gaining access to thespecific mobile resource location database. In many instances the mobileresource is used interchangeably with an asset, recognizing that therelevance is for each of the assets that move or have a dynamic physicallocation. A vector is calculated and provided that fills in the locationgaps of time based on probability models driven by either a) likeassets, b) specific history across time and space domain for thespecific asset, c) and the mode that is known closest in time to the gapin known location. The mode as noted earlier can include parameters suchas category of activity (e.g., eating, cleaning, exercising, patient,etc.). The need to have access to location data varies by activity type(e.g., shopping, eating, driving, exercising, etc.), mode type (e.g.,work, home, vacation, healthcare) and also by the purpose of obtainingdata (e.g., predicting purchasing product|venue, eating venue, wellnessindicator, financial wellness stress indicator, travelpreferences|routing, exercise, etc.).

Turning to FIG. 3, FIG. 3 depicts the data structure, in this instancefor a mobile resource at location A302, such that the locationdetermination is first improved by at least 5% accuracy throughcalibration function 401 as a function of at least one known location(i.e., having precise location e.g., accuracy of less than 0.5 meter orpreferably less than 0.25 meter or particularly preferred less than 2 cmaccuracy) and/or through wireless history records 400 in proximityrelevant to this same location A302 (e.g., repetitive movement through ageofence in close proximity to this same location A302 yields wirelesshistory records such as triangulation via known wireless communicationmethods including WiFi or Bluetooth as known in the art) establish alocation probability 28.1 that is a function of wired locations in whichtasks occur at a known time, known wireless geofence in which mobileresource wireless signal strength is known to be within said wirelessgeofence creating a subset geofence in which location A302 must bewithin, known prior task geofence having an associated known timeyielding a calibration point for future use of wireless history records400, known scheduled next task that provides an increased probability ofmovement towards the location in which that known scheduled next taskmust occur (i.e., if the mobile resource is on a 2^(nd) floor and theknown scheduled next task is also on the 2^(nd) floor then movement fromthe known prior task geofence to the known scheduled next task has avery high probability of also being on the 2^(nd) floor and further inbetween the known locations of the known prior task geofence and thesubsequent known scheduled next task), and an uncertain wireless (i.e.,such as a repetitive placement of GPS on a different floor, such thatthe occurrence of the known scheduled next task ‘geofence’ actual timein combination with the occurrence of the known prior task geofenceenables future (as well as retroactive) calibration of location vectordespite the otherwise significant error in location. Once the moreprecise location is determined (whether for future or retroactively) amore precise location vector is created. The knowledge of preciselocation and location vector, particularly as a function of time, isextremely valuable for establishing intimate and highly personal (whenthe mobile resource is physically connected to a human) information andthus highly susceptible to privacy intrusions. Therefore, placement ofprecise location and location vectors requires both methods to enableauthorized access to this information yet contextually segmented so asto ensure only proper authorized access to this information even thoughit is available wherever access to the distributed ledger database 10 isavailable. This data is therefore encrypted using either or both a rulesdatabase 20.1 and/or mode database 20.2 with further data manipulationsuch that the precise location and location vectors are entered into thedistributed ledger database with known (in accordance to the rulesdatabase 20.1 and mode database 20.2 respectively by a rules offsetprofile 24.1 that is a function of at least one time and location and amode offset profile 24.2 also as a function of at least one time andlocation.

For those tasks/events/operations/activities of different mode(s),access to other specific mode data is both by permission and still thenonly in summary form which includes: a) projected times in which mode isexpected within a range of start and end time with associatedprobabilities, with optional probability summary of other modes (if havepermission) within that same start and end time; b) frequency ofoccurrence within a specified geofence and within a range of start andend time; c) # a and # b can be answered by including other specificallyknown factors including current location of resource/person and thelocation of the requesting provider as well as additional knownresources/people co-located with the mobile resource in which locationdata is being requested.

Multi-Mode for Multi-Mobile Resource for Combinatorial Mode andSelective Relative Location Access

To protect against fraud, the inventive system is able to receive aprobability function for a specific mobile resource location beingwithin a specific geofence at a specific time (this could be such thatan internet protocol address “IP address” established in the executionof a purchase transaction establishes a location represented by ageofence). When the purchase transaction is performed by a mobileresource linked by location proximity to a known and specific stationarytelephone number, then user identifier information or at least afrequent geofence presence is established (which depending on time ofday can be indicative of either a home address or work address).Furthermore, the use of real-time location data from a mobile phone incombination with historic data (particularly using repetitive knownlocation data, when the mobile accuracy is high, provides a probabilityof being in proximity to a home address, work address, and/or otherimportant geofences in that person's life e.g., school, daycare,synagogue, exercise location). These high-probability geofence,location, or sub-area information is critical to establishing a profilefor the mobile resource, and then ultimately a mode for that mobileresource.

Another feature of the system is the use of concurrent movement ofmobile resources (via precise location of mobile resource or indirectlyvia co-located mobile phones) to establish a relationship between aresource and a set of people (i.e., individuals linked to other mobileresources) such as a husband's mobile phone moving concurrently with awife's mobile phone). This linkage is important to establishing (or atleast impacting) the mobile resource mode to a higher probability familyor vacation mode. Co-located and linked mobile resources, especiallywhen the identity (or profile) of at least one of the mobile resourcesis known. This scenario enables the profile, and therefore the mode ofthe mobile resource by being able to differentiate between which mobileresource is performing an activity as compared to the other mobileresource attending such an event (e.g., a parent attending a soccergame). One exemplary use case is for e-commerce transactions such thatwhen the knowledge of which mobile resource is a participant as opposedto an attendee is a vital parameter to establish the probability thateach of the mobile resources would be conducting an e-commercetransaction at a specified time and at a specified location. Thefollowing shared-vehicle is an exemplary use case for this feature ofthe invention.

In a shared resource environment, specifically a vehicle (or anywireless device having a known identity), it is possible to then link aperson(s) mobile number with the person traveling within that vehicle.The query to the cellphone company(ies) is what vectors are movingtogether. A likely scenario is that the shared resource has a known GPSbut on a different cellular network as the vehicle. A first mobileresource is linked to at least one other mobile resource in which thecombination of the two mobile resources establishes not only aninterrelationship between the two mobile resources but in fact thefurther combination of each mobile resource's mode with the other mobileresource's mode provides a combined mode that in fact creates a combinedmode for the interrelationship of the first mobile resource and thesecond mobile resource. It is particularly useful such that knowledge ofthe second mobile resource (i.e., passenger) prior mode or predictiveknowledge of a projected future mode establishes a preferred mode forthe combined first mobile and second mobile resource. A specific exampleis establishing preferred music selection of a passenger (or a set ofpassengers) during a trip from a first location to a second location. Inthe event the prior mode for the passenger was exercise then it can beanticipated that the combined mode could be a relaxing mode, or if theprojected future mode is exercise it can be anticipated that thecombined mode could be an upbeat mode. The collective mode of allconcurrent (i.e., traveling together) mobile resources in fact is acritical element to establishing the preferences of the passengerswithin the shared vehicle. Yet another exemplary is in a mobile resourcesuch as food delivery shuttle such that the execution of a delivery taskis linked to the second mobile resource (i.e., the patient) and furthercould require the presence of a third mobile resource (e.g., dietician)authorized for the delivery task as such may be required in instances ofsevere food allergies. Another exemplary would be respectively thedelivery of pharmaceuticals to a specific patient such that the type ofpharmaceutical may dictate a nurse practitioner as in the instance of acontrolled substance (e.g., narcotic).

Yet another exemplary where precise location knowledge is importantincludes credit card transactions. A credit card company desires to knowthe probability that a specific first mobile resource (i.e., a customer)having a known mobile phone number (which could be another second mobileresource, or in fact in many instances the mobile phone is a proxy forthe first mobile resource i.e., it's customer. As noted earlier, this isa vital feature of fraud protection and particularly benefits from theretroactive location gap “filling” for online e-commerce transactions.However, improper access to location data can lead to unintendedconsequences therefore the DRS should limit/control access to data asthe use of historic location data for a specific person when combinedwith specific time domain information enables one to predict theprobability of that same mobile resource not being present at a specificlocation within a specific time window (e.g., anticipate good times tosteal).

Yet retroactive location gap “filling” is not adequate for in-personbusiness transactions as a real-time requirement is necessary since theproduct delivery/transfer takes place concurrently with the credit cardtransaction. In order to validate the credit card belongs to (or isauthorized by) the proper mobile resource the DRS must access a morecomprehensive location analysis and NOT just the real-time location. Dueto privacy concerns, the business authorizing the transaction mustaccess the location information for that specified mobile resourcelinked to the credit card owner and therefore a mode restricted accessis important (and in fact a provision to provide a relative location asopposed to an absolute location such as if the credit card owner isactually in the hospital which is very intimate knowledge that isn'trelevant to the nature of a credit card transaction taking placeelsewhere i.e., an non-authorized transaction. Another aspect of thecredit card transaction could be whether a first mobile resource i.e.,customer is in close proximity to a second mobile resource i.e.,customer's spouse and credit card owner.

Of particular interest would be a) is this “second” mobile resourcei.e., person traveling alone?, b) is the “first” mobile resource i.e.,vehicle an autonomous vehicle or just a shared-vehicle actually beingdriven by the second mobile resource or is the second mobile resource apassenger and the third mobile resource is the driver?, c) are thesecond and third mobile resources related to each other or are they justtraveling together (both are useful in also establishing or predicting amode for the respective mobile resource or in fact the mode for thecombination relationship), and if not specifically known what is thetypical probability that they are driving or a passenger? Another likelyscenario is that a specific mobile resource's real-time locationdetermination signal may not be known (or at least not precisely enough)such that probability is necessary to determine whether the respectivemobile resource is within a specific geofence? And then what is theprobability that the mobile resource is doing a specific activity? Allof these scenarios can take advantage of inventive features ranging fromretroactive location gap filling, multi-mode, and distributed ledger.

Location, location, location is important. But even more important isthe location convergence of at least two mobile resources including inapproximately the same time domain within the location similaritythreshold limit. A fundamental objective is to determine the mode of aspecific device operator (i.e., second mobile resource)? The mode(s) ofoccupants (i.e., third, fourth, . . . mobile resource) within a geofence(which can be a store i.e., static location or a first mobile resourcei.e., vehicle? If this person is in a vehicle, what is the probabilitythat they are sick? If this sick person is the driver then additionalcleaning procedures may need to take place (which could make a secondarytask of performing an interval non-scheduled task into a primary taskhaving a precise scheduled complete by time. A higher probability ofsickness increases the emphasis on scheduling a primary task ofperforming a cleaning, as well as potentially issuing an alert to othermobile resources converging in the approximately same location and timedomains.

Dynamic Mode Driven Data Distribution

As noted earlier, mode is a critical element to limiting access to datathrough utilization of the mode as an encryption/decryption key. Yet itis anticipated that a dramatic increase in mobile resources,particularly autonomous and/or semi-autonomous vehicles, will create avast network of roaming and mobile computing resources (hereinaftercollectively referred to a dynamic cluster computer “DCC” network. Theinventive location features are enabling features in a dynamic anddistributed datacenter. Mode for virtually all mobile resources not onlyestablishes the encryption/decryption key but additionally establishesthe probability of data required for a given time and location domain.The DCC network can also include stationary datacenters, but DCCleveraging distributed ledger and mobile computing resources offer lowerlatency times, lower transmission distances, inherent redundancy,inherent data security, and enhanced capital asset utilization. The actof moving a mobile resource from one location to another requires anincredible amount of on-board computing power, with virtually all of itnot being utilize when the mobile resource is not being utilized.Furthermore, when the first mobile resource (i.e., the shared-vehicle)is carrying an at least one second mobile resource (i.e., passenger)again in autonomous mode there is a significant increase (relative tocurrent operation of a driver in the physical act of driving atraditional non-autonomous vehicle) in data consumption. The combineddata access requirements of the vehicle and the passenger provide anopportunity for the on-board data storage to be specific to the specificmobile resources travelling and furthermore the on-board data storagecan be more precisely targeted through the mode of the respective mobileresources. When the mobile resource is an electric shared vehicle, theon-board electrical energy storage (i.e., battery) is sized for mobilityand NOT computation therefore there is no incremental cost associatedwith otherwise a traditional datacenters backup power generator(s). Dueto the relatively lower physical security of the DCC network, theutilization of the inventive distributed ledger by location mode (orbetter yet the combined mode for the vehicle and passenger) overcomesthe physical security deficiencies with superior encryption/decryptionsecurity.

The DCC features methods to dynamically move data to projected locationssuch that the data that is moved is both as a function of mode, wherebythe data-server that is closest or distance compensated to make itotherwise less expensive. Data that is pushed to a specific data-serveris also contingent on security policy, privacy, encryption, and type ofdata. Encryption key is at least comprised of user identifier+mode,preferably geofence+user identifier+mode. In the optimal mode, the dataset is leverages container technology as known in the art specificallyutilizing caching and prefetching of information to the projecteddynamically projected locations (that vary as a f(t, mode).

Turning to FIG. 9, FIG. 9 depicts the hardware components for theexecution of the mobile resource 690 leveraging the integral dynamicrouting and scheduling system server 4000. The mobile resource 690 is atleast in intermittent, though preferably continuous, communication withthe dynamic routing & scheduling system server 4000 via the wirelessnetwork interface 3112. The server 4000 has a system bus 3110 furthercomprised of system memory 3021, operating system 3022, file system3023, program data 3025, processor 3100 to execute applications 3024 ascoordinated by a controller 3111 with optional display 3113 to anoperator coordinating the full execution of the system server (includingwhere the display 3113 is remote from the system server 4000 as known inthe art. The system server 4000, though traditional in its nature, thesystem server 4000 integrates the fundamental and inventive feature ofsegmenting access to location specific data with authentication as knownin the art (e.g., blockchain) though the data itself is encryptedthrough the encryption (or decrypted through the decryption) engine3203. The encryption key is further a function of at least one of themode as established by the mode engine 3201 for each instance of thelocation as established by the location engine 3205. The privacy rules,as coordinated with the privacy rules engine 3297, establish a secondlayer of access control by dynamic encryption as a direct function ofmode and location. As noted in prior figures, the location utilized isnot always precise, therefore the probability engine 3204 isinstrumental in improving the accuracy of the location beyond the bestavailable wireless methods (including triangulation) as known in the artthrough the aforementioned leverage of historic calibration.Additionally, gaps in precise location determination are filled in usingthe retroactive location engine 3296 to improve not only locationprecision but also pathways in which the mobile resource 690 travelsusing the pathway engine 3202. As known in the art data is communicatedto networked and distributed hardware, including placing the data incontainers as known in the art, through the network data and containerexchange engine 3200. The mobile resource 690 can have a co-located orremote operator, such that the mobile resource execution of primary andsecondary tasks can be modified via the operator resource 697 using theonboard remote computer 4001. The remote computer 4001 has identicalmajor components in functionality, though understood that thespecifications and performance may be different, plus an augmentedreality or virtual reality display 3113.1 providing real-time feedbackto persons within its current operating vicinity. Each mobile resource690 has a range of tasks in which it is certified and qualified ascoordinated by the qualification engine 3299. Each mobile resource alsohas a 360-degree noise impact map as both a function of specificoperations to fulfill primary and/or secondary tasks and range frommobile resource 690 to other people (e.g., guests, patients, staff,etc.) present as the mobile resource passes by in its movement from afirst location to a second location. The noise impact mapping engine3399 modulates at least one of the pathways in which the mobile resourcetakes and/or the execution of secondary tasks along the taken pathway,and/or the speed within the pathway, and/or the speed/rate of taskexecution so as to minimize the noise impact. The location mappingengine 3210 coordinates the precise pathway executed by the mobileresource 690 such that execution of both primary tasks and secondarytasks are effectively achieved, that being such that secondary tasks aresequenced in accordance to the task operation engine 3211 with furtheroperating parameters as established by the noise impact mapping engineafter the qualification engine 3299 validates the ability of the mobileresource to adequately perform its assigned tasks.

A significant advantage of the DCC, besides utilization of an otherwiseunderutilized computational resource, is a significant reduction inwireless bandwidth requirements as the DCC by leveraging location modeas a function of time, is the on-board data storage reduces by at least10% (as compared to non-onboard data storage) and preferably greaterthan 50% (and particularly preferred greater than 90%) wirelessbandwidth requirements to access data/information that is “custom”curated for the on-board mobile resources (i.e., passengers). Theknowledge of on-board mobile resource(s) and the mode for eachrespective on-board resource is critically important as a mobileresource that is a passenger (as compared to a driver) will requiresignificantly higher access to their respective curated on-board datastorage. Knowledge that a person took public transportation today (i.e.,location and time domains) with the nature of current and futuretasks/activities being known (or at least a probability profile can beestablished) also establishes a real-time and retroactive determinationof mode and therefore a probability profile for then current or futuretasks (i.e., e-commerce transaction using credit card). Importantly theuse of public transportation provides additional information such asprecise task/event location with time domain so as to enhance theperformance of the retroactive location gap filling procedure. Superiorknowledge of location, mode, and convergence points of multipleco-located mobile resources enables a more accurate prediction of futuretasks/activities for the balance of the day (or at least until such timeas next destination is reached).

Knowledge of the nature (or probabilistic determination) of activity ofa passenger/driver on a shared-vehicle (or any known location/modecombination) enables the DRS to determine the nature of current activity(which is also very important in anticipating the next task/activity fora specified future time domain). For example, the knowledge orprobability of any given task/event such as driving, sleeping, eating,exercising, in building, in vehicle including airplane establishes therelative probability of future tasks/events.

Yet another feature of mobile resources converging in both the time andlocation domains is modulating the execution of predominantly secondarytasks, though primary tasks as well are anticipated, such that theperformance of the task has a reduced noise impact amongst co-locatedmobile resources. This implementation of this feature is as follows inthe next section.

Noise Impact Abatement Routing

An inventive feature of the dynamic routing system is taking intoaccount noise tolerance for each sub-area (as defined earlier, beingpart of a location within a geofence) also a function of noise ofactivity, proximity to what it passes by and noise sensitivity of whatit passes by, occupancy of what it passes by including the nature ofactivity in which the occupant is doing, time of day, and distance ofwhat it passes by, and function of noise distribution as a function ofdistance and direction. Using this, the system can also adjust the ratein which it does the intermediate activity as rate of activity canchange the noise creation profile.

The system utilizes real-time, historic, and retroactively correctedimage mapping to create an updated probability map within the sub-area(that is within the location and geofence) assembled by at least onecamera or sensor device but preferably from a stitched image assembledfrom multiple camera or sensors from multiple mobile resource equipmentplus additional fixed position camera or sensors. The system has atleast two maps of the sub-area, one of which is the contaminantprobability map and the other is the mobile resource pathway map ofassembled vectors (plus task coverage offset preferably represented by avector having a width to cover the task coverage offset). A particularlypreferred embodiment has an at least 3′ sub-area map for each knowndisease state being tracked such that contaminant source pathwayprovides a time stamped (i.e., utilize the time color code forincidence, which can optionally be overlaid by intensity representingthe frequency of exposure) vector. Each of the maps are preferablyrepresented by the inventive feature of the vector having a time (colorcode) within the space domain.

It is understood that noise propagation is a function of the directionof travel for the mobile resource, in addition to the operatingcondition in which the task performing device on the mobile resource iscontrolled (e.g., speed of vacuum fan, air flow speed and volume,electric motor, etc.). The system, whether it be issuing a command tothe mobile resource to regulate or limit noise (through such meansincluding speed control), as a function of both time and space domain.It is a feature of the system to utilize a sub-area map utilizing thesame vector color code to represent relative noise levels within theoverall sub-area. It is understood that a distinct noise level map canpreferably exist as a function of time (e.g., time of day range),occupancy of adjoining sub-area or geofence or location, as well asadditional parameters that are location specific such as within geofenceproduced noise such as a TV or guest visitation within a hospital orhotel room.

Definition of modes and where they are important WITHIN a geofence andfor segmenting database. NOT for transition from one mode to another.

HAI—contaminated, cleaned, potentially contaminated, need cleaning;{time since change of mode}

Definition of a combined mode, such that the mode is dependent onmultiple co-located users each having a mode and then having a groupmode as a function of each individual mode.

It is understood that the invention includes and anticipates known inthe art individual non-contiguous anti-infection spreading methodsintegrated into a linked and coupled system to increase time and spacedomain continuity for at least one disease state.

Although the invention has been described in detail, regarding certainembodiments detailed herein, other anticipated embodiments can achievethe same results. Variations and modifications of the present inventionwill be obvious to those skilled in the art and the present invention isintended to cover in the appended claims all such modifications andequivalents.

What is claimed is:
 1. A mobile resource dynamic location and datasegmenting system comprised of an at least one mobile resource whereinthe at least one mobile resource executes a first task at a first knownlocation having a first task completion time and a second task at asecond known location having a second task completion time and an atleast one intermediate task having an intermediate task completion time,wherein the at least one mobile resource at least one intermediate taskcompletion time is after the first task completion time and before thesecond task completion time, whereby the mobile resource performs eachof the first task, second task, and at least one intermediate task usinga computer system having a communication link to determine a travelmovement pathway between the first known location and the second knownlocation, whereby the travel movement pathway is comprised of a vectorof an actual movement pathway of the mobile resource and wherein thevector has an embedded gradient on a time domain whereby the time domainranges from an earliest starting time to a latest ending timedetermining an at least one convergence point between the mobileresource and the first known location and the second known location, orbetween a first mobile resource of the at least one mobile resource anda second mobile resource of the at least one mobile resource, whereinthe vector has an at least one gap in travel movement between the firstknown location and the second known location, whereby the system isfurther comprised of an at least one historic record of location asdetermined by concurrent wireless location determination means, andwhereby the concurrent wireless location determination is calibratedcreating an increased location precision by at least 5% of the at leastone gap in travel movement, and whereby the travel movement pathway withthe calibrated increased location precision is used to retroactivelyimprove an accuracy of the vector for a future utilization of the vectortravel movement of the mobile resource.
 2. The mobile resource dynamiclocation and data segmenting system according to claim 1 whereby thefuture utilization of the vector travel movement is an on-line orderfulfillment to prevent or limit fraud by means of retroactively usingthe calibrated increased location precision.
 3. The mobile resourcedynamic location and data segmenting system according to claim 1 wherebythe mobile resource travel movement from and between the first knownlocation to the second known location is dynamically routed to minimizea noise impact within an at least one geofence whereby the at least onegeofence is in between the first known location and the second knownlocation.
 4. The mobile resource dynamic location and data segmentingsystem according to claim 1 whereby the vector of travel movement of themobile resource is encrypted and contained within a distributed ledgerand whereby the encryption utilizes an encryption key based on aspecific mode within a location geofence.
 5. The mobile resource dynamiclocation and data segmenting system according to claim 1 whereby thevector of travel movement of the mobile resource is comprised of areal-time location and a non-real-time location adjustment whereby thenon-real-time location adjustment improves the vector's accuracy by atleast 5%.
 6. The mobile resource dynamic location and data segmentingsystem according to claim 1 whereby the vector of travel movement of themobile resource is dynamically altered for privacy using a locationoffset wherein the location offset is a function of an at least one oftime, a mode within a geofence location, and a rules-based logiccombining at least two of the time, the geofence location, and the modewithin the geofence location.
 7. The mobile resource dynamic locationand data segmenting system according to claim 1 whereby the mobileresource moves at a speed during its travel movement from a first knownlocation to a second known location and whereby the speed is dynamicallyaltered as a function of the at least one intermediate task having anintermediate task effectiveness and a noise impact within an adjoininggeofence between the first known location and the second known location.8. The mobile resource dynamic location and data segmenting systemaccording to claim 1 wherein the vector has an at least one probabilitygradient geofence for the travel movement between the first knownlocation and the second known location.
 9. A mobile resource dynamiclocation and data segmenting system comprised of an at least one mobileresource wherein the at least one mobile resource executes a first taskat a first known location having a first task completion time and asecond task at a second known location having a second task completiontime and an at least one intermediate task having an intermediate taskcompletion time and an intermediate task location, wherein the at leastone mobile resource at least one intermediate task completion time isafter the first task completion time and before the second taskcompletion time, whereby the mobile resource performs the first task andthe second task using a computer system having a communication link todetermine a travel movement pathway between the first known location andthe second known location, whereby the travel movement pathway iscomprised of a vector of an actual movement pathway of the mobileresource and wherein the vector has an embedded gradient on a timedomain whereby the time domain ranges from an earliest starting time toa latest ending time determining an at least one convergence pointbetween the mobile resource and the first known location and the secondknown location, wherein the vector has an at least one gap in travelmovement between the first known location and the second known location,whereby the travel movement pathway is used to retroactively ensure onlyauthorized access between the first known location and the second knownlocation.
 10. The mobile resource dynamic location and data segmentingsystem according to claim 9 wherein the vector has an at least oneprobability gradient geofence for the travel movement between the firstknown location and the second known location.
 11. The mobile resourcedynamic location and data segmenting system according to claim 9 wherebythe vector of travel movement of the mobile resource is encrypted andcontained within a distributed ledger and whereby the encryptionutilizes an encryption key based on a specific mode within a locationgeofence.
 12. The mobile resource dynamic location and data segmentingsystem according to claim 9 whereby the vector of travel movement of themobile resource is comprised of a real-time location and a non-real-timelocation adjustment whereby the non-real-time location adjustmentimproves the vector's accuracy by at least 5%.
 13. A mobile resourcedynamic location and data segmenting system comprised of an at least onemobile resource wherein the at least one mobile resource executes afirst task at a first known location having a first task completion timeand a second task at a second known location having a second taskcompletion time and an at least one optional intermediate task having anintermediate task completion time and an intermediate task location,wherein the at least one mobile resource at least one optionalintermediate task completion time is after the first task completiontime and before the second task completion time, whereby the mobileresource performs each of the first task and the second task using acomputer system having a communication link to determine a travelmovement pathway between the first known location and the second knownlocation, whereby a first specific data is segmented to the first knownlocation and a second specific data is segmented to the second knownlocation, whereby the first specific data and second specific data issegmented as a distributed ledger, and whereby the distributed ledgeruses encryption and decryption keys based on an at least one knownlocation or an at least one location mode.
 14. The mobile resourcedynamic location and data segmenting system according to claim 13whereby the encryption and decryption keys are dynamic and vary by acombination of an at least one known location and an at least one knownlocation mode.
 15. The mobile resource dynamic location and datasegmenting system according to claim 13 whereby access to the firstspecific data is further segmented to the at least one location mode andthe first known location or a first intermediate task location and an atleast one intermediate task mode, and whereby access to a secondspecific data is further segmented to the at least one location mode andthe second known location or a second intermediate task location and anat least one intermediate task mode, and whereby the encryption anddecryption keys are dynamic and vary by an at least one intermediatelocation or an at least one intermediate location mode.
 16. The mobileresource dynamic location and data segmenting system according to claim13 whereby access to the first specific data is segmented to the firstknown location or a first intermediate task location, and whereby accessto a second specific data is segmented to the second known location or asecond intermediate task location, whereby the first specific data andsecond specific data is segmented as a distributed ledger, and wherebythe distributed ledger uses dynamic encryption and decryption keys basedon an at least one known location or an at least one intermediatelocation, or an at least one location mode.
 17. The mobile resourcedynamic location and data segmenting system according to claim 13whereby the travel movement pathway is comprised of a vector of anactual movement pathway of the mobile resource and wherein the vectorhas an embedded gradient on a time domain whereby the time domain rangesfrom an earliest starting time to a latest ending time determining an atleast one convergence point between the mobile resource and the firstknown location and the second known location, or between a first mobileresource of the at least one mobile resource and a second mobileresource of the at least one mobile resource, wherein the vector has anat least one gap in travel movement between the first known location andthe second known location, whereby the system is further comprised of anat least one historic record of location as determined by concurrentwireless location determination means, and whereby the concurrentwireless location determination is calibrated creating an increasedlocation precision by at least 5% of the at least one gap in travelmovement, and whereby the travel movement pathway with the calibratedincreased location precision is used to retroactively improve anaccuracy of the vector for a future utilization of the vector travelmovement of the mobile resource.
 18. The mobile resource dynamiclocation and data segmenting system according to claim 13 whereby thevector of travel movement of the mobile resource is encrypted andcontained within a distributed ledger and whereby the encryptionutilizes an encryption key based on a specific mode within a locationgeofence.
 19. The mobile resource dynamic location and data segmentingsystem according to claim 13 whereby the vector of travel movement ofthe mobile resource is dynamically altered for privacy using a locationoffset wherein the location offset is a function of an at least one oftime, a mode within a geofence location, and a rules-based logiccombining at least two of the time, the geofence location, and the modewithin the geofence location.
 20. The mobile resource dynamic locationand data segmenting system according to claim 13 wherein the vector hasan at least one probability gradient geofence for the travel movementbetween the first known location and the second known location.